Journal of Applied Science, Engineering, Technology and Management

Experimental Investigation of Strength Enhancement Between PVA Fibre & PVA Powder Modified Ferrocement Mortar Used Retrofitting on Challenging Structural Members

2026-07-15T07:07:08+00:00

Strengthening of existing challenging structural members like reinforced concrete (RC) columns is a critical requirement for extending safe life span in structural rehabilitation. Ferrocement jacketing is a widely adopted retrofitting technique due to its efficiency and ease of application. Retrofitting technique can be further modified by adding different admix like fibre materials, silica fumes, fly ash, polymer etc with cement mortar. Enhanced ferrocement jacketing with polyvinyl alcohol (PVA) fibre reinforced mortar has been extensively studied for its crack resistance and ductility upgradation [9][10]. While limited research exists on the use of PVA powder modified mortar as a practical alternative. This study presents an experimental investigation comparing the performance of RC columns retrofitted, using 0.5% PVA fibre mortar and 0.5% PVA powder modified mortar. A total of six RC column specimens were casted, cracked, retrofitted and tested under axial compression. The results indicate that both systems exhibit comparable ultimate load capacity, while differences were observed in crack initiation and workability. The study highlights the research gap justification of the feasibility of PVA powder as a practical alternative to PVA fibre reinforcement in ferrocement jacketing applications.

Planar Sub-THz Bandpass Filter Based on a Cross-Arrow Slot FSS for Radar and Secure Communication Applications

2026-07-15T07:24:14+00:00

A cross-arrow slot frequency selective surface (FSS) is proposed for sub-terahertz (sub-THz) bandpass filtering, of-fering enhanced spectral performance over conventional cross-shaped designs. Full-wave simulations in CST Studio Suite demonstrate a sharp transmission peak (S21) of −0.3 dB at 0.175 THz, with a −10 dB bandwidth of 25 GHz and a quality factor (Q) of 6.5. The design achieves excellent impedance matching, as indicated by a reflection coefficient (S11) of −37 dB. The arrow-shaped slot extensions promote strong current confine-ment, resulting in high spectral selectivity and low insertion loss. Parametric tuning enables resonance shifting across the 0.15 THz – 0.25 THz range, covering a wide portion of the sub-THz spectrum. All simulations were done in the CST microwave suite, and the results were validated in HFSS software. The proposed filter is also suitable for military sub-THz systems, including secure high-data-rate communication links, radar front-ends, and spectrum-selective sensing applications.

Photovoltaic Panel Defect Detection System

2026-07-15T16:05:59+00:00

Detecting defects in photovoltaic (PV) panels is important for maintaining their performance, especially in large-scale solar farms where manual inspection is time-consuming and inefficient. This work presents a simple and practical deep learning solution that makes this process easier and more accessible by using images captured through UAVs (drones), enabling efficient monitoring of large areas. A customized YOLOv8m-based model is designed to automatically detect and classify defects, even with a relatively small dataset of around 700–800 images across five categories. To handle the lack of data, class-based GAN augmentation is used to increase data variety and improve balance between classes. The model is further improved with additions like a C2f-PSA module for better attention and a custom Multi-Scale Feature Fusion (MCFF) block to help detect small and hard-to-spot defects. While these improvements enhance the model’s ability to learn features, they also make it more sensitive to dataset size and design choices, sometimes affecting accuracy. To make the system useful in real-world situations, a user-friendly web application and dashboard are developed using Streamlit, allowing users to upload images, see detected defects with their severity levels, and easily understand the results. The system also enables users to generate and download detailed reports with annotated images, making it a complete and practical solution for efficient PV panel monitoring and maintenance in large solar installations.

PAVE: Pothole Avoidance and Vision Engine

2026-07-15T16:26:20+00:00

The rapid growth of transportation systems has increased the demand for safer and smarter road infrastructure. Road defects such as potholes and improper recognition of traffic signals are major causes of accidents and traffic inefficiencies. This paper presents PAVE (Pothole Avoidance and Vision Engine), an intelligent system that integrates pothole detection and traffic signal recognition using computer vision and deep learning techniques. The proposed system utilizes a YOLOv8-based model to detect potholes and identify traffic signals such as red and green lights from images and real-time video streams. A dataset consisting of road images is used to train the model for accurate classification and detection. The system enables efficient processing and real-time performance. The integrated approach improves driver awareness by providing timely alerts for road hazards and traffic signals. This enhances road safety, reduces accidents, and supports advanced driver assistance systems (ADAS). Experimental results demonstrate that the system achieves high accuracy and performs effectively in real-time environments.

Enhancing Construction Efficiency of Educational Building Through BIM Based Clash Detection

2026-07-15T16:43:26+00:00

The construction of educational facilities involves complex interactions between architectural, structural, and MEP systems, often resulting in design conflicts when conventional practices are followed. Building Information Modelling (BIM) provides an integrated digital framework that enhances coordination and enables early detection of such conflicts. This study investigates the application of BIM-based clash detection to improve the efficiency and reliability of construction processes in educational buildings. A comprehensive BIM model is developed using Autodesk Revit, incorporating architectural, structural, and MEP components. The discipline-specific models are federated within Navisworks Manage to perform systematic clash detection. Identified conflicts are categorized into hard clashes, soft (clearance) clashes, and workflow clashes. The detected issues are analyzed, and appropriate design modifications are implemented to achieve coordination among all systems. The implementation of BIM-based clash detection significantly reduces design inconsistencies, minimizes rework, and enhances interdisciplinary coordination. The findings demonstrate improved constructability, optimized project scheduling, and better resource utilization. This study establishes BIM as an effective tool for proactive conflict resolution, contributing to improved project performance and quality in the construction of educational infrastructure.

Predictive Modeling of Stress Levels Using Physiological and Lifestyle Factors

2026-07-16T09:25:49+00:00

Stress has emerged as a critical factor that affects both physical and mental health in today’s fast paced lifestyle. Early detection of stress levels can enable timely interventions and preventive healthcare. This study presents a data driven approach for stress level classification using physiological and lifestyle features derived from a publicly available dataset. Key variables include age, occupation, cholesterol level, sleep quality, physical activity, and other health related metrics. A Random Forest Classifier is used to model and classify individuals into three stress levels, i.e., low, moderate, and high. The model achieves a classification accuracy of 0.74, with an averaged F1 score of 0.71, indicating a strong generalization performance. Feature importance analysis reveals that occupation, age, and cholesterol level are the most influential predictors of stress. Deeper analysis shows that individuals in high response profes sions, within the age range of 30 to 40 years, and with elevated cholesterol levels are more prone to high stress. Moreover, the model offers insight into the relative importance of each feature, enhancing the interpretability and potential clinical utility. The proposed model demonstrates that integrating machine learning with wearable and lifestyle data serves as a powerful tool for proactive stress management and personalized healthcare.

GateSense: A Smart Continuous Attendance System

2026-07-16T09:47:21+00:00

GATESENSE is an IoT-based classroom attendance system designed to address two key limitations of conventional methods: the loss of approximately 10 minutes of lecture time per hour consumed by manual roll calls, and the inability to verify continuous student presence beyond an initial check-in. The system employs an ESP32 DevKit V1 as the central microcontroller, integrating an R307S optical fingerprint sensor for biometric entry authentication, a dual IR sensor array for bidirectional movement detection, and an RC522 RFID reader for mandatory exit validation. A finite state of machine implemented in embedded C++ governs all state transitions. Movement events are pushed in real time to a Firebase cloud database, where a Vite-based web dashboard provides period-wise attendance analytics for students and administrators. Testing over 100 controlled trial cycles at FISAT demonstrated a directional detection accuracy of 98% and an average cloud synchronisation latency of 1.5 seconds. The system reduced active faculty effort for attendance to zero during entry, recovering approximately 70 minutes of instructional time per seven-period academic day. These results suggest that the proposed architecture offers a cost-effective and scalable alternative to existing RFID-only or facial-recognition-based systems.

Engineering Innovation for Intelligent Systems, Sustainable Infrastructure and Applied Technologies

2026-07-21T18:09:33+00:00

Engineering research today is being reshaped by the rapid convergence of artificial intelligence, digital technologies and sustainable design. As disciplines increasingly overlap, there is a growing focus on solutions that are not just technically sound, but also practical, scalable and genuinely responsive to real-world needs — from resilient infrastructure and clean energy to smart mobility, healthcare and education